List of material on Classification: September 8, 2009
1. Classification: what and why needed.
2. Classification is a two-step process
3. Supervised and unsupervised learning
4. Issues / criteria for selecting classifiers
5. Decision (classification) tree (DT): how it works; how to build (we cover Quinlan’s
algorithm); illustrative example; some related issues
6. Classification model evaluation: (Performance assessment:
 Training, validation, test / generalization errors
 Holdout method (training, validation, test sets)
 Cross-validation
7. Case studies using RBF classifiers (details of RBF classifiers will be discussed later):
Cancer class determination(microarray data analysis), Pima Indians diabetes
classification, Soybean disease classification; (Software)module criticality assessment